Initial commit.

.gitignore

@@ -0,0 +1,4 @@
+.idea/
+venv/
+tmp/
+*.bmp

README.md

@@ -1,8 +1,8 @@
 ---
 title: Super Image
-emoji: ⚡
-colorFrom: gray
-colorTo: indigo
+emoji: 🔍
+colorFrom: green
+colorTo: purple
 sdk: gradio
 app_file: app.py
 pinned: false

app.py

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+import torch
+import gradio as gr
+from random import randint
+from pathlib import Path
+from super_image import ImageLoader, EdsrModel, MsrnModel, MdsrModel, AwsrnModel, A2nModel, CarnModel, PanModel, \
+    HanModel, DrlnModel, RcanModel
+
+title = "super-image"
+description = "State of the Art Image Super-Resolution Models."
+article = "<p style='text-align: center'><a href='https://github.com/eugenesiow/super-image'>Github Repo</a>" \
+          "| <a href='https://eugenesiow.github.io/super-image/'>Documentation</a> " \
+          "| <a href='https://github.com/eugenesiow/super-image#scale-x2'>Models</a></p>"
+
+
+def inference(img, scale_str, model_name):
+    _id = randint(1, 1000)
+    output_dir = Path('./tmp/')
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_file = output_dir / ('output_image' + str(_id) + '.jpg')
+    scale = int(scale_str.replace('x', ''))
+    if model_name == 'EDSR':
+        model = EdsrModel.from_pretrained('eugenesiow/edsr', scale=scale)
+    elif model_name == 'MSRN':
+        model = MsrnModel.from_pretrained('eugenesiow/msrn', scale=scale)
+    elif model_name == 'MDSR':
+        model = MdsrModel.from_pretrained('eugenesiow/mdsr', scale=scale)
+    elif model_name == 'AWSRN-BAM':
+        model = AwsrnModel.from_pretrained('eugenesiow/awsrn-bam', scale=scale)
+    elif model_name == 'A2N':
+        model = A2nModel.from_pretrained('eugenesiow/a2n', scale=scale)
+    elif model_name == 'CARN':
+        model = CarnModel.from_pretrained('eugenesiow/carn', scale=scale)
+    elif model_name == 'PAN':
+        model = PanModel.from_pretrained('eugenesiow/pan', scale=scale)
+    elif model_name == 'HAN':
+        model = HanModel.from_pretrained('eugenesiow/han', scale=scale)
+    elif model_name == 'DRLN':
+        model = DrlnModel.from_pretrained('eugenesiow/drln', scale=scale)
+    elif model_name == 'RCAN':
+        model = RcanModel.from_pretrained('eugenesiow/rcan', scale=scale)
+    else:
+        model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=scale)
+    inputs = ImageLoader.load_image(img)
+    preds = model(inputs)
+    output_file_str = str(output_file.resolve())
+    ImageLoader.save_image(preds, output_file_str)
+    return output_file_str
+
+
+torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/baby_mini_d3_gaussian.bmp',
+                               'baby.bmp')
+torch.hub.download_url_to_file('http://people.rennes.inria.fr/Aline.Roumy/results/images_SR_BMVC12/input_groundtruth/woman_mini_d3_gaussian.bmp',
+                               'woman.bmp')
+
+gr.Interface(
+    inference,
+    [
+        gr.inputs.Image(type="pil", label="Input"),
+        gr.inputs.Radio(["x2", "x3", "x4"], label='scale'),
+        gr.inputs.Dropdown(choices=['EDSR-base', 'EDSR', 'MSRN', 'MDSR', 'AWSRN-BAM', 'A2N', 'CARN', 'PAN', 'HAN',
+                                    'DRLN', 'RCAN'],
+                           label='Model')
+    ],
+    gr.outputs.Image(type="file", label="Output"),
+    title=title,
+    description=description,
+    article=article,
+    examples=[
+        ['baby.bmp', 'x2', 'EDSR-base'],
+        ['woman.bmp', 'x3', 'MSRN']
+    ],
+    enable_queue=True
+    ).launch(debug=True)